Get the canonical record for a part: identity, manufacturer, description, category, lifecycle, and government classification (NSN/FSC) when present. Sourced from the PartsTable canonical read model.
| Name | Required | Description | Default |
|---|---|---|---|
| partNumber | Yes | Part number to look up |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries the transparency burden. It adds useful context by naming the data source (PartsTable canonical read model) and notes that classification is returned 'when present.' However, it does not disclose behavior like read-only status, error handling, or query limits, leaving gaps typical of a minimally adequate description.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is two sentences, front-loaded with the core action ('Get the canonical record') and wastes no words. It lists fields and source efficiently.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a simple single-parameter lookup with no output schema, the description conveys the record scope, source, and optional classification field. It lacks explicit mention of not-found behavior or return format, but these are minor for a straightforward read tool, making it nearly complete.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100%: the only parameter, partNumber, is already described as 'Part number to look up.' The tool description adds no further parameter semantics, so the baseline score of 3 applies.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool's function: retrieve the canonical record for a part, listing specific data fields (identity, manufacturer, description, category, lifecycle, NSN/FSC). This distinguishes it from siblings like search-parts (searching) or get-substitutes (alternatives).
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The context of 'canonical record' and 'canonical read model' implies this is the authoritative exact-match lookup, giving clear usage context. However, it does not explicitly mention when not to use it or contrast with siblings, so it misses the explicit exclusions that would merit a 5.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Add one secure layer between your agents and this server.
Each tool has a clearly distinct purpose: retrieving a single part record, finding substitutes, normalizing part numbers, batch searching, and validating existence. There is no overlap or ambiguity.
All tool names follow a consistent verb_noun pattern (e.g., get-part, normalize-pn, validate-pn) using snake_case. The naming is predictable and easy to understand.
With 5 tools, the server is well-scoped for a read-only parts catalog. Each tool serves a essential function without being too few or overly numerous.
The tool set covers the core operations needed for a parts table: record retrieval, cross-reference lookup, normalization, batch existence check, and validation. There are no obvious gaps given the read-only design.
